Blog 06

Q&A with Erin Connolly-Strong PhD, Head Medical Affairs Scipher Medicine via BiopharmaDive

Multiple classes of drugs exist for treating rheumatoid arthritis (RA) patients. But close to 90% of patients failing methotrexate are prescribed the world’s largest selling drug class, tumor necrosis factor-α inhibitor (TNFi) therapies – even though the majority don’t respond adequately.

A new study published in Rheumatology and Therapy demonstrates that PrismRA, a molecular signature test using a simple tube of blood, can predict the likelihood of not achieving a clinically meaningful response of at least a 50% improvement in symptoms in targeted therapy-naïve patients and now, those patients already exposed to the TNF-inhibitor class or currently on an anti-TNF agent.

Erin Connolly-Strong, PhD, Head of Medical Affairs at Scipher Medicine, co-author of the study explains the results and science behind PrismRA.

You’re one of the co-authors on a new paper published in Rheumatology and Therapy on treatment response in RA. What were the goals of this study?

The objective was to prospectively demonstrate that PrismRA^® can predict the likelihood of not achieving response, low disease activity, or remission with TNFi therapy across multiple clinical outcome measures at 3 and 6 months, including ACR50, ACR70, DAS28-CRP, and CDAI.

This was a prospective multi-center trial, which is important when we think about the objectives around the study, since a main goal was predicting the likelihood of not achieving response to the TNF-inhibitor class of medications, which are commonly prescribed for RA. We wanted to do that prospectively, but also challenge ourselves by looking at multiple disease activity measures. Our previous study had been retrospective, and we defined treatment response primarily with the ACR50 measure.

In this new study we really wanted to push the envelope, so the results could be very actionable for physicians. We demonstrated that by using the standard clinical measures, we can predict response accurately at 3 and 6 months.

Our previous study validated the PrismRA molecular signature in samples from the CorEvitas registry’s CERTAIN study cohort. In this earlier study we prospectively collected both the patients’ molecular and clinical data.

Can you give us a sense of where Scipher intends to go with its RA research?

One of the positive things about RA therapeutics is choice. There are multiple classes of drugs approved for the disease. But the American College of Rheumatology (ACR) guidelines can be a challenge when it comes to selecting targeted therapy, since the professional society doesn’t rank one therapy over another or suggest an ideal prescribing order.

The dilemma for physicians is having to work through that, as well as the challenge of formularies. So, really when we think about a major problem that needs solving in RA, it’s that despite having all of these choices, anti-TNF therapy is the go-to.

Two out of 3 targeted treatment-naïve patients will also not have an adequate response to anti-TNF medications. We need to get patients to the right therapy as quickly as possible, so that they can start to experience symptom improvement, a better quality of life, and hopefully remission.

The big question is who should not get TNF inhibitor therapy? That’s why we designed a molecular signature response classifier to rule out anti-TNF agents for those patients who are unlikely to respond to them so they can go on an alternative FDA-approved therapy faster and avoid unnecessary delays, dose escalations, or cycling.

Examining the problem more closely, there are 2 main types of patients for whom PrismRA can help inform treatment decisions – first, the targeted therapy-naïve patient and then second, all of the other patients who have already been exposed to the TNF-inhibitor class or are currently on an anti-TNF agent.

Providers need to know if TNF is the right pathway, as well, so that’s part of what we were looking to solve in this trial – to expand our intended use beyond just that naïve population, but also to really be able to provide accurate predictions in that targeted therapy population as a whole.

What biological markers are you measuring to rule out certain patients for these therapies?

This is one of the most fascinating areas of our research. When you go to scientific meetings, there’s always a new biomarker being presented, but they rarely come to fruition. That’s because identifying individual biomarkers often doesn’t consider a patient’s full biology, since they’re usually just a genomic or protein marker.

We’re unique in that we have what we call our “molecular signature.” We find a set of biological markers that capture individuals’ genetic makeups and their disease behavior. The molecular signature does this by including RNA, protein, and other features that reflect a patient’s biology. For PrismRA specifically we include protein expression, RNA expression, patient and provider-reported outcomes, and patient characteristics. So, it’s all of those things together that give us the complete biology of that patient. That signature has components that are important in RA and that help us predict the patient’s likely response to anti-TNF treatment.